Neural Prognostication of Thyroid Carcinoma Recurrence an Interdisciplinary Inquiry into Predictive Modelling and Computational Oncology DOI

R. Sireesha,

K. Nandini,

Srimathkandala Ch V. S. Vyshnavi

et al.

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 503 - 516

Published: Jan. 1, 2024

Language: Английский

Guidance on the construction and selection of relatively simple to complex data-driven models for multi-task streamflow forecasting DOI
Trung Duc Tran, Jongho Kim

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: 38(9), P. 3657 - 3675

Published: July 17, 2024

Language: Английский

Citations

2
Unraveling the factors behind self-reported trapped incidents in the extraordinary urban flood disaster: a case study of Zhengzhou City, China DOI
Hongbo Zhao, Yangyang Liu, Yue Li

et al.

Cities, Journal Year: 2024, Volume and Issue: 155, P. 105444 - 105444

Published: Oct. 9, 2024

Language: Английский

Citations

2
Welding defect detection based on phased array images and two-stage segmentation strategy DOI
Yan Chen, Deqiang He,

Suiqiu He

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102879 - 102879

Published: Oct. 1, 2024

Language: Английский

Citations

2
Attention-based deep learning framework for urban flood damage and risk assessment with improved flood prediction and land use segmentation DOI
Zuxiang Situ,

Qisheng Zhong,

Jianliang Zhang

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: unknown, P. 105165 - 105165

Published: Dec. 1, 2024

Language: Английский

Citations

1
A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes DOI Creative Commons
Jiarui Yang, Kai Liu, Ming Wang

et al.

International Journal of Disaster Risk Science, Journal Year: 2024, Volume and Issue: 15(5), P. 754 - 768

Published: Oct. 1, 2024

Abstract Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations enhancing physical connectivity and interpretability. This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise rapid prediction of pluvial flooding processes enhance reliability results. The began by generating a rainfall-inundation dataset using WCA LISFLOOD-FP, CNN-WCA model was trained outputs from LISFLOOD-FP WCA. Subsequently, pre-trained applied simulate flood caused 20 July 2021 rainstorm Zhengzhou City. predicted inundation spatial distribution depth closely aligned those mean absolute error concentrated within 5 mm, time only 0.8% LISFLOOD-FP. displays strong capacity for accurately changes depths area at susceptible points flooding, Nash-Sutcliffe efficiency values most flood-prone exceeding 0.97. Furthermore, is better than obtained CNN. additional constraints exhibits reduction around 34% instances discontinuity compared Our results prove CNN multiple has significant potential improve prediction.

Language: Английский

Citations

0
Rapid urban inundation prediction method based on numerical simulation and AI algorithm DOI

Xinxin Pan,

Jingming Hou, Guangzhao Chen

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132334 - 132334

Published: Nov. 1, 2024

Language: Английский

Citations

0
Neural Prognostication of Thyroid Carcinoma Recurrence an Interdisciplinary Inquiry into Predictive Modelling and Computational Oncology DOI

R. Sireesha,

K. Nandini,

Srimathkandala Ch V. S. Vyshnavi

et al.

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 503 - 516

Published: Jan. 1, 2024

Language: Английский

Citations

0